Monthly Archives: November 2017

Typically the IBM Z Mainframe is recognized as the de facto System Of Record (SOR) for storing Mission Critical data. It therefore follows for generic business applications, DB2, IMS (DB) and even VSAM could be considered as database servers, while CICS and IMS (DC) are transaction servers. Extracting value from the Mission Critical data source has always been desirable, initially transferring this valuable Mainframe data source to a Distributed Platform via ETL (Extract, Transform, Load) processes. A whole new software and hardware ecosystem was born for these processes, typically classified as data warehousing. This process has proved valuable for the last 20 years or so, but more recently the IT industry has evolved, embracing Artificial Intelligence (AI) technologies, ultimately generating Machine Learning capabilities.

For some, it’s important to differentiate between Artificial Intelligence and Machine Learning, so here goes! Artificial Intelligence is an explicit Computer Science activity, endeavouring to build machines capable of intelligent behaviour. Machine Learning is a process of evolving computing platforms to act from data patterns, without being explicitly programmed. In the “what came first world, the chicken or the egg”? You need AI scientists and engineers to build the smart computing platforms, but you need data scientists or pseudo machine learning experts to make these new computing platforms intelligent.

Conceptually, Machine Learning could be classified as:

An automated and seamless learning ability, without being explicitly programmed

The ability to grow, change, evolve and adapt when encountering new data

An ability to deliver personalized and optimized outcomes from data analysed

When considering this Machine Learning ability with the traditional ETL model, eliminating the need to move data sources from one platform to another, eradicates the “point in time” data timestamp of such a model, and any associated security exposure of the data transfer process. Therefore, returning to the IBM Z Mainframe being the de facto System Of Record (SOR) for storing Mission Critical data, it’s imperative that the IBM Z Mainframe server delivers its own Machine Learning ability…

Machine Learning for z/OS provides a simple framework to manage the entire machine learning workflow. Key functions are delivered through intuitive web based GUI, a RESTful API and other programming APIs:

In conclusion, the Machine Learning for z/OS solution delivers the requisite framework for the emerging Data Scientists to collaborate with their Business Analysts and Application Developer colleagues for delivering new business opportunities, with smarter outcomes, while lowering risk and associated costs.